Introduction to Algorithms
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A framework for multimedia content abstraction and its application to rushes exploration
Proceedings of the 6th ACM international conference on Image and video retrieval
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Organizing rushes video by visually similar setting
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Dimensionality reduction for heterogeneous dataset in rushes editing
Pattern Recognition
Comparison of content selection methods for skimming rushes video
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Video rushes summarization utilizing retake characteristics
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
THU-intel at rushes summarization of TRECVID 2008
TVS '08 Proceedings of the 2nd ACM TRECVid Video Summarization Workshop
Detecting and clustering multiple takes of one scene
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Evaluating detection of near duplicate video segments
Proceedings of the ACM International Conference on Image and Video Retrieval
ELVIS: Entertainment-led video summaries
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In audiovisual post-production users are confronted with large amounts of redundant unedited raw material, called rushes. Viewing and organizing this material is a crucial but time consuming task. This paper describes an approach for creating skimmed versions of the rushes video based on the elimination of unusable content and clustering of takes. Typically multiple, but slightly differing takes of the same scene can be found in the rushes video. We propose a method for clustering takes of one scene shot from the same camera position. It uses a variant of the LCSS algorithm to find matching subsequences in sequences of extracted features from the source video. The approach is evaluated by two subjective measures for the quality of the skim and by measuring the overlap between items found in the source video and the skim.